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technologyreview.com·3h ago
AI Research
arXiv CS.AI·May 25·8 min read
SSDAU: Structured Semantic Data Augmentation for Joint Entity and Relation Extraction
TL;DR
Joint Entity and Relation Extraction (JERE)—identifying named entities and their relationships in text—suffers from poor generalization when training data is limited or low-quality. SSDAU (Structured Semantic Data Augmentation) preserves semantic structure during data augmentation by segmenting text by entities, capturing context-aware embeddings, and filtering topic drift using BERTTopic.
✦ Why It Matters
Engineers can use SSDAU to generate higher-quality training data for information extraction systems, improving model robustness in production environments with domain shift.
Key Takeaways
- Use SSDAU for better data augmentation in JERE tasks.
- Segment text based on entity labels for effective augmentation.
- Incorporate BERTTopic to maintain topic consistency.
- Leverage contextual embeddings to improve entity differentiation.
How It Works
SSDAU segments input text according to entity labels, allowing for focused augmentation. It employs an encoder to capture the semantic features of entities, ensuring that the augmented data retains the original context.
By restructuring entities semantically and fusing contextual embeddings with traditional similarity scores, SSDAU effectively distinguishes between semantically similar entities. The BERTTopic model is then applied to filter out irrelevant topics, maintaining topic consistency and reducing ambiguity.